http://196.220.128.81:8080/xmlui/handle/123456789/177 2026-04-08T10:47:37Z http://196.220.128.81:8080/xmlui/handle/123456789/5640 EFFECTS OF PRE-MONSOON BIOMASS BURNING AEROSOLS ON RAINFALL CHARACTERISTICS OVER WEST AFRICA TEEDA, NJIE This study investigate the effects of pre-monsoon biomass burning aerosols (BBA) on rainfall characteristics over West Africa. The specific objectives aimed to be achieved are; estimating the distribution of pre-monsoon biomass burning aerosols (BBA) over the study area; analyzing rainfall characteristics over the study area; evaluating the capability of the regional climate model (WRF-Chem) on capturing BBA effect on monsoon rainfall and determining the influence of BBA on rainfall characteristics and cloud formation. AERONET Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) data were used to estimate the temporal distribution of AOD and AE and classification of aerosol types over the five selected AERONET sites namely, Agoufou, Banizoumbou, Dakar, IER_Cinzana and Ilorin. Rainfall data from ERA5 for the period of 1998-2021 was used to evaluate rainfall characteristics such as distribution, variability, normal, wet, and dry (drought) condition and rainfall trend over the five selected sites. Simulations were also run using WRF-Chem model to evaluate the capability of the model in capturing BBA and to investigate the effect of BBA on rainfall and cloud formation. The study found out that AOD peaks in March-June in all the sites except Ilorin where it peaks in January. The maximum values of AE were in December-January for all the sites except Ilorin where maximum AE value was in August. This shows the presence of fine mode aerosols. It has been found that desert dust aerosol was the dominant aerosol in all the sites throughout the study period. The normal and wet climatic conditions were dominant for both annual and seasonal rainfall at all the sites during the study period. High rainfall variability throughout the study period and all the seasons with no trend for annual and negative trend for MAM and JJA season. This means that it is easier to use mean to predict rainfall performance for the annual rainfall but difficult the seasonal rainfall performance in the study area. Lastly, the study found out that the WRF-Chem model overestimated the rainfall characteristics and the BBA radiative effects either increase or decrease rainfall amount depending on the period/season over West Africa. The WRF-Chem model also underestimates the values of Outgoing Longwave radiation (OLR) and the BBA radiative effects increased the rate of convective cloud formation over West Africa PhD 2023-06-01T00:00:00Z http://196.220.128.81:8080/xmlui/handle/123456789/5639 EVALUATION OF HEAT WAVE PREDICTABILITY SKILLS OF NUMERICAL WEATHER MODELS RAJI, IBRAHEEM AYOMIDE Significant changes are being experienced in the climate system due to the unprecedented rate of global warming. This has resulted in the increased frequency of weather extreme events such as heatwave occurrence in the Northern Nigeria. In order to mitigate the effects of heatwaves, early warning systems are needed to be implemented. Insufficient knowledge about the performance of the models is partly a factor that hinders the development of such systems. This study thus, addresses the gap by assessing the predictability skills of sub-seasonal to seasonal numerical weather model over different time lead and as well improves the predictability skills through the incorporation of deep learning to post-process the model output at a 30-day lead period. The Excess Heat Index (EHI) was used to detect heatwave occurrence over the study area, using both observational and forecast data from selected S2S models at 5 -, 7 -, 15 -, and 30 – days lead time. Metrics employed to evaluate the skills of the models are; the Anomaly correlation coefficient (ACC), and Symmetric External Dependency Index (SEDI) with each evaluating different strength of the models. The result of the analysis shows that the three models considered in this study overestimated the heat wave frequency in the region. This results in reduced reliability of the models in the region. Further analyses shows that the use of deep learning to bias correct the model output increases the forecast reliability in the region significantly. M.Tech. 2023-04-01T00:00:00Z http://196.220.128.81:8080/xmlui/handle/123456789/5638 COMPOSITE INDEX-BASED EARLY WARNING SYSTEM FOR DROUGHT MONITORING IN THE NIGER RIVER BASIN OF WEST AFRICA OKPARA, JUDDY NGOZICHUKWUKA This study seeks to develop a comprehensive and integrated Early Warning System (EWS) for concurrent monitoring of meteorological, agricultural, and hydrological droughts in the Niger Basin of West Africa. The specific objectives of the research are as stated below. First, to establish threshold(s) for defining and detecting moderate to severe drought events in the Niger River. To accomplish the task, Baseline Assessment Analysis (BAA) using combined tool of Standardized Precipitation Index (SPI 6-month) and Percentile Rank. Second, to determine the thresholds beyond which a dry spell changes and becomes actual drought in the Niger Basin. This was achieved through combination of baseline assessment analysis (BAA), the standardized precipitation index (SPI 2-month) and percentile rank analyses. The SPI-2month has weak correlation with the ecosystems, because dry spell is a precursor to drought and not real drought. Third, to develop a Niger Basin Drought Monitoring (NBDM) scheme or Drought Empirical Model (DREM). The task was accomplished through development of an Objective Blend of Drought Disaster Burden Index (OBDDBI), using as inputs, the Standardized Effective Precipitation Index (SEPI 6-month), Soil Moisture Index (SMI) and Streamflow Index (SFI) to form one big picture composite index with ‘all-in-one’ drought initiation thresholds. Then, characterize the basin’s moderate to severe drought events, using the established drought thresholds from the DREM. Fourth, to evaluate and validate the performance of NBDM to ascertain its reliability or usefulness. This task was achieved by first evaluating the Composite Drought Index (CDI) ability to reproduce other input indices using some statistical tools. This was followed by the validation of the obtained CDI outputs from the DREM against ENSO chronology and known drought induced famine impacts reports in the drought chronology of the region. Furthermore, all the analyses including the development of the DREM and establishment of all-in-one dry spells and drought thresholds were done using hydrometeorological reanalysis datasets from 1980-2016. Results based on the DREM composite drought index (CDI) all-in-one thresholds, revealed that the onset of drought of moderate intensity, either meteorological, agricultural or hydrological can be defined with thresholds of range -0.26 to -1.19 over three consecutive months depending on the location. In terms of percentiles, it corresponds to threshold of 20th percentile. With DREM, therefore, these are thresholds to trigger alert for the occurrence of different biophysical forms of drought events of moderate intensity in the Niger River Basin. However, with SPI-6months all-in-one thresholds, the droughts of moderate or worst intensity occur when the sum of cumulative precipitation deficits equal or falls below the critical thresholds of -0.37 to -1.08 or less over three consecutive months depending on the location. This corresponds to 20th percentiles monthly precipitation deficits; thereby, resulting in late drought onset detection relative to CDI. On the other hand, dry spells occur in the Niger River Basin when the sum of cumulative precipitation deficits of SPI 2-month equal or falls below the thresholds of range -0.22 to -0.45 over 2 or more consecutive months, depending on the location. This corresponds to 35th percentile monthly precipitation deficits. However, phase change from dry spell to actual drought condition, is initiated as the cumulative precipitation deficits increases with the SPI 2-month values falling below the critical dry spell threshold of - 1.20 (corresponding to 10th percentile) and becoming fully established drought at threshold of - 1.66 (corresponding to 5th percentile). The potential of the CDI thresholds being used operationally, as all-in-One thresholds for concurrent monitoring of all biophysical forms of drought was investigated. Additionally, evaluation of the results showed significant values of the index of agreement (d) of 0.953 and 0.978, 0.844 and 0.898, 0.914 and 0.932, 0.890 and 0.910 between CDI and Soil Moisture Index (SMI) and Streamflow Index (SFI) over Upper Niger, Inland Niger, Middle Niger and Lower Niger sub-basins respectively. Validation results further revealed success rate of range 67 to 100% based on the past records of drought disaster events captured by the DREM CDI, and 62 to 77% based on ENSO-induced drought records. In conclusion, DREM could make an effective and reliable drought monitoring and early warning decision support tool. PhD 2022-11-01T00:00:00Z http://196.220.128.81:8080/xmlui/handle/123456789/5637 PREDICTION OF DRY SPELLS DURING GROWING SEASON WITH RESPECT TO MAIZE CROP IN NIGERIA NNOLI, NNADOZIE OKONKWO The prediction of dry spells during growing season with respect to maize crop was carried out in nine stations which include Calabar, Warri, Ibadan, Ilorin, Lokoja, Makurdi, Yelwa, Kaduna and Yola in Nigeria. The main data, which span 1971 through 2013, used for this work were: daily rainfall, maximum and minimum temperature, 0600 and 1500 GMT relative humidity, wind speed at 2 metre level and sunshine hours. They were sourced from the Nigerian Meteorological Agency (NiMet), Lagos. The same data set (excluding rainfall and sunshine hours) from 0.125° ERA INTERIM Reanalysis, 1979-2013, and daily 0.25° horizontal resolution 3B42 rainfall from Tropical Rainfall Measuring Mission (1998-2013) were obtained to serve as supplement to NiMet data. The daily and average reference evapotranspiration (ETo) were computed for tne selected stations for maize crop 118 days gestation using the method described in FAO Irrigation and Drainage Publication 56 and Penman-Monteith combination equation. Dry spell frequencies were determined during maize growth. The percentage frequency of dry spell lengths categorized as < 5 days, 5-10 days, 11-15 days, 16-20 days and >20 days were determined on successive ten-day periods (dekads) from the onset of growing season to 120 days for the 43 years for maize crop . The mid-growing season stage critical dry spell onset dates, lengths and their trends were determined. Mann Kendall tests were performed on the trends of the onset dates and lengths of dry spell to ascertain their statistical significance. The initial and mid-season critical dry spell lengths in categories: <5, 5-10, 11-15, 16-20 and >20 days were analyzed in relation to maize yields. Yearly predictions of the onset dates and lengths of critical dry spells (predictands or classes) were made only during the mid-growing season stage with the use of Artificial Neural Network (ANN). The predictors (attributes) are mean values of maximum and minimum temperatures, mean temperature, 0600 and 1500 GMT relative humidity, wind speed at 2 metre above soil surface, vsunshine hours, net radiation and reference evapotranspiration. About 70% of the data set was deployed for training while 30% was for testing. Based on cross-correlation analysis which measured the relationship between the predictors (attributes) and the predictands (classes), seven different models were put forward for the prediction purpose. For the 9 out of the stations evaluated, the occurrence of more (less) critical dry spells of lengths 5-10 and 11-15 days during the mid-season and 5-10 days only during initial stages leading to less (more) maize yield was generally associated with El-Nino (La-Nina) years. The percentage frequency of mid-season spell lengths of category 5-10 days ranged from 4-31% for nine stations. The number of days maize farmers in all the stations could expect first and second mid-season critical dry spell occurrences after planting ranged from 35-82 and 50-86 days respectively. The most suitable model for the prediction of critical dry spell onset dates and lengths for the nine stations was Model 1 (9 parameters) followed by Models 2 (8 parameters), 3 (7 parameters) and 5 (5 parameters). Prediction lead times for first and second critical dry spell onset dates generally ranged from 2 weeks to 2 months in the nine stations. Assessment on the efficiency of the most suitable models (for onset dates and lengths) for the 9 stations based on statistics indicate that the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe coefficient of efficiency (NSE), the Wilmott's Index of Agreement (WIA), RMSE-Observations Standard Deviation Ratio (RSR) and Prediction error margin ranged between 0.96 and 3.31; 0.58 and 0.93; 0.51 and 0.90; 0.82 and 0.98; 0.30 and 0.69; -4.56 to 4.89 days respectively. These results showed the capability of ANN to predict yearly onset dates and lengths of mid-growing season critical dry spells for maize crop. These findings will aid strategic and yearly planning of agricultural operations for enhanced maize crop yield in Nigeria. PhD 2023-06-01T00:00:00Z